A Survey of solution techniques for the partially observed Markov decision process
Annals of Operations Research
The Witness Algorithm: Solving Partially Observable Markov Decision Processes
The Witness Algorithm: Solving Partially Observable Markov Decision Processes
Efficient dynamic-programming updates in partially observable Markov decision processes
Efficient dynamic-programming updates in partially observable Markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Exploiting belief bounds: practical POMDPs for personal assistant agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Winning back the CUP for distributed POMDPs: planning over continuous belief spaces
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Efficient maximization in solving POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Indefinite-horizon POMDPs with action-based termination
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Piecewise linear dynamic programming for constrained POMDPs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Policy iteration for decentralized control of Markov decision processes
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Bounded policy iteration for decentralized POMDPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Risk-sensitive planning in partially observable environments
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Implementation techniques for solving POMDPs in personal assistant agents
ProMAS'05 Proceedings of the Third international conference on Programming Multi-Agent Systems
Topological value iteration algorithms
Journal of Artificial Intelligence Research
The Skyline algorithm for POMDP value function pruning
Annals of Mathematics and Artificial Intelligence
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We present a major improvement to the incremental pruning algorithm for solving partially observable Markov decision processes. Our technique targets the cross-sum step of the dynamic programming (DP) update, a key source of complexity in POMDP algorithms. Instead of reasoning about the whole belief space when pruning the cross-sums, our algorithm divides the belief space into smaller regions and performs independent pruning in each region. We evaluate the benefits of the new technique both analytically and experimentally, and show that it produces very significant performance gains. The results contribute to the scalability of POMDP algorithms to domains that cannot be handled by the best existing techniques.